{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/CoreTheGreat/HBPU-Machine-Learning-Course/blob/main/ML_Chapter3_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lPboLx_o0UxI"
   },
   "source": [
    "# 第三章：分类\n",
    "湖北理工学院《机器学习》课程资料\n",
    "\n",
    "作者：李辉楚吴\n",
    "\n",
    "笔记内容概述: 绘制混淆矩阵 Confusion Matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ifpm7Sql4U09"
   },
   "source": [
    "## Step 1: 数据准备\n",
    "\n",
    "使用make_moons生成虚拟数据构建分类任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "sM-ziKb94S9_",
    "outputId": "9a8bd59b-1ed4-47e3-e118-cee1849a610a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "\n",
    "# Generate moon-shaped data\n",
    "X, Y = make_moons(n_samples=1000, noise=0.25, random_state=42)\n",
    "\n",
    "# Split X and Y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Plot the data\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='red', label='Positive - Train')\n",
    "ax.scatter(X_test[y_test == 1, 0], X_test[y_test == 1, 1], color='orange', label='Positive - Test')\n",
    "ax.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='blue', label='Negative - Train')\n",
    "ax.scatter(X_test[y_test == 0, 0], X_test[y_test == 0, 1], color='green', label='Negative - Test')\n",
    "ax.set_xlabel('Feature 1', fontsize=label_size)\n",
    "ax.set_ylabel('Feature 2', fontsize=label_size)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Set legend fontsize\n",
    "plt.legend(prop={'size': 14})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: 使用逻辑回归、SVM、决策树和随机森林模型进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BWPSDyOq5qOO",
    "outputId": "9aed06f2-67a9-4a6f-f00c-d91554c3a260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model trained successfully...\n",
      "Support vector machine trained successfully...\n",
      "Decision tree trained successfully...\n",
      "Random forests trained successfully...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Definition\n",
    "mdl_lr = LogisticRegression() # Logistic Regression\n",
    "mdl_svm = SVC() # Support Vector Machine\n",
    "mdl_dt = DecisionTreeClassifier() # Decision Tree\n",
    "mdl_rf = RandomForestClassifier(n_estimators=100) # Random Forests\n",
    "\n",
    "# Model Training\n",
    "mdl_lr.fit(X_train, y_train) # Logistic Regression\n",
    "print('Logistic regression model trained successfully...')\n",
    "\n",
    "mdl_svm.fit(X_train, y_train) # Support Vector machine\n",
    "print('Support vector machine trained successfully...')\n",
    "\n",
    "mdl_dt.fit(X_train, y_train) # Decision Tree\n",
    "print('Decision tree trained successfully...')\n",
    "\n",
    "mdl_rf.fit(X_train, y_train) # Random Forest\n",
    "print('Random forests trained successfully...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BtGNbqlSKnC-"
   },
   "source": [
    "## Step 3: 混淆矩阵 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'seaborn'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m \u001b[38;5;66;03m# To display confusion matrix\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconfusion_matrix\u001b[39m(y_pred, y):\n\u001b[0;32m      4\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m    y_pred - predict classes\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;124;03m    y - actual classes\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns # To display confusion matrix\n",
    "    \n",
    "def confusion_matrix(y_pred, y):\n",
    "    '''\n",
    "    y_pred - predict classes\n",
    "    y - actual classes\n",
    "    '''\n",
    "    # Get the number of classes\n",
    "    n_classes = len(np.unique(y))\n",
    "    \n",
    "    # Initialize the confusion matrix with zeros\n",
    "    cm = np.zeros((n_classes, n_classes))\n",
    "    \n",
    "    # Fill the confusion matrix\n",
    "    for pred, actual in zip(y_pred, y):\n",
    "        cm[pred][actual] += 1\n",
    "    \n",
    "    return cm\n",
    "\n",
    "def cm_disp(cm, title='Confusion Matrix', save_fig=False):\n",
    "    ''' \n",
    "    Display confusion matrix\n",
    "    '''\n",
    "    global label_size, ticklabel_size\n",
    "    \n",
    "    # Create a figure and axis\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    \n",
    "    # Use seaborn to create a heatmap without color bar\n",
    "    sns.heatmap(cm, annot=True, fmt='.0f', cmap='Blues', ax=ax, annot_kws={'size': ticklabel_size}, cbar=False)\n",
    "    \n",
    "    # Set ticklabels\n",
    "    ax.set_xticklabels(['Positive', 'Negative'])\n",
    "    ax.set_yticklabels(['Positive', 'Negative'], rotation=90)\n",
    "    \n",
    "    # Set labels and title with custom font sizes\n",
    "    ax.set_xlabel('Predicted labels', fontsize=label_size)\n",
    "    ax.set_ylabel('True labels', fontsize=label_size)\n",
    "    ax.set_title(title, fontsize=label_size)\n",
    "    \n",
    "    # Set tick label font size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "    \n",
    "    if save_fig:\n",
    "        plt.savefig(f'{title.replace(\" \", \"_\")}_confusion_matrix.png', dpi=300, bbox_inches='tight')\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "    \n",
    "# Get confusion matrix of all models\n",
    "# Predictions for each model\n",
    "y_pred_lr = mdl_lr.predict(X_test)\n",
    "y_pred_svm = mdl_svm.predict(X_test)\n",
    "y_pred_dt = mdl_dt.predict(X_test)\n",
    "y_pred_rf = mdl_rf.predict(X_test)\n",
    "\n",
    "# Confusion matrices for each model\n",
    "cm_lr = confusion_matrix(y_pred_lr, y_test)\n",
    "cm_svm = confusion_matrix(y_pred_svm, y_test)\n",
    "cm_dt = confusion_matrix(y_pred_dt, y_test)\n",
    "cm_rf = confusion_matrix(y_pred_rf, y_test)\n",
    "\n",
    "# Display the confusion matrices\n",
    "save_flag = False\n",
    "cm_disp(cm_lr, title='Logistic Regression', save_fig=save_flag)\n",
    "cm_disp(cm_svm, title='Support Vector Machine', save_fig=save_flag)\n",
    "cm_disp(cm_dt, title='Decision Tree', save_fig=save_flag)\n",
    "cm_disp(cm_rf, title='Random Forest', save_fig=save_flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: 计算各模型的准确率 Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cm_lr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 14\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m accuracy\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Accuracy of each model\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m acc_lr \u001b[38;5;241m=\u001b[39m getAccuracyFromCM(\u001b[43mcm_lr\u001b[49m)\n\u001b[0;32m     15\u001b[0m acc_svm \u001b[38;5;241m=\u001b[39m getAccuracyFromCM(cm_svm)\n\u001b[0;32m     16\u001b[0m acc_dt \u001b[38;5;241m=\u001b[39m getAccuracyFromCM(cm_dt)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'cm_lr' is not defined"
     ]
    }
   ],
   "source": [
    "def getAccuracyFromCM(cm):\n",
    "    # Calculate total number of samples\n",
    "    total = np.sum(cm)\n",
    "    \n",
    "    # Calculate number of correct predictions (sum of diagonal elements)\n",
    "    correct = np.trace(cm)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = correct / total\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "# Accuracy of each model\n",
    "acc_lr = getAccuracyFromCM(cm_lr)\n",
    "acc_svm = getAccuracyFromCM(cm_svm)\n",
    "acc_dt = getAccuracyFromCM(cm_dt)\n",
    "acc_rf = getAccuracyFromCM(cm_rf)\n",
    "\n",
    "# Print accuracies\n",
    "print(f\"Logistic Regression Accuracy: {acc_lr:.4f}\")\n",
    "print(f\"Support Vector Machine Accuracy: {acc_svm:.4f}\")\n",
    "print(f\"Decision Tree Accuracy: {acc_dt:.4f}\")\n",
    "print(f\"Random Forest Accuracy: {acc_rf:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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